基于深度强化学习的户内变电站通风降噪优化设计

doi:10.11930/j.issn.1004-9649.202211051

摘要/Abstract

摘要： 针对户内变电站运行发热导致温度过高引发安全风险，以及采取相关散热措施可能导致噪声扰民的问题，提出一种基于有限元仿真和深度强化学习的户内变电站进风口设计参数优化方法。以此对户内变电站通风系统进风口位置大小进行优化设计，使其获得最优通风降噪效果。首先，通过有限元分析法对其温度场、流体场和声场进行仿真建模；然后，基于大量仿真数据，采用卷积神经网络建立温度和噪声的预测模型；最后，考虑噪声约束，利用基于最大熵强化学习框架的SAC算法，以变电站室内温度最低为目标对进风口设计参数进行优化求解。研究结果表明，经过优化后的进风口设计方案能够有效降低变电站室内温度，同时使噪声满足国家规范要求。

关键词: 户内变电站, 有限元法, 通风降噪, 强化学习, 优化设计

Abstract: In view of the equipment safety problems caused by the high operation temperature of the indoor substations and the disturbing noise problems caused by relevant heat dissipation measures, this paper proposes a method for optimizing the design parameters of indoor substation air inlets based on finite element simulation and deep reinforcement learning to obtain the optimal ventilation and heat dissipation effects. Firstly, the temperature field, fluid field and sound field of the indoor substations are modeled and simulated with the finite element analysis method. Then, based on a large number of simulation data, the convolutional neural network is used to establish the prediction model of temperature and noise. Finally, considering the noise constraint, the maximum entropy reinforcement learning framework based SAC algorithm is used to optimize the design parameters of the air inlets with the goal of minimizing the indoor temperature of the substation. The research results show that the optimized air inlet design scheme can effectively reduce the indoor temperature in the substation, and at the same time make the noise meet the requirements of national regulations.

Key words: indoor substation, finite element method, ventilation and noise reduction, reinforcement learning, optimization design

汤锦慧, 伍发元, 支妍力, 毛梦婷, 代小敏. 基于深度强化学习的户内变电站通风降噪优化设计[J]. 中国电力, 2023, 56(1): 96-105,118.

TANG Jinhui, WU Fayuan, ZHI Yanli, MAO Mengting, DAI Xiaomin. Optimization Design of Indoor Substation Ventilation and Noise Reduction Based on Deep Reinforcement Learning[J]. Electric Power, 2023, 56(1): 96-105,118.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202211051

https://www.electricpower.com.cn/CN/Y2023/V56/I1/96

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